Machine-Learning Data Helps Global Development Organizations Monitor & Evaluate Programs
Technology company Fraym partners with the World Bank & the African Development Bank
Data generated by machine-learning is now being used by preeminent international development organizations to inform programmatic evaluations and to help them make data-driven decisions. Fraym, a technology company, recently worked with the World Bank and the African Development Bank to employ advanced geospatial data and uncover insights that traditional data analysis could not reveal.
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Spatial knowledge of population dynamics enables fast, precise, and comprehensive assessments—and illustrates the effects of investments on the most pressing global development needs. Fraym’s founders worked in global development at the highest levels of the U.S. government, and now their original use case which identified a need for localized data, has been proven and scaled. Ben Leo, CEO and Co-Founder of Fraym reflects, “Our company is deeply passionate about and connected to the global development space. I’m humbled that our technology contributes to the missions of organizations like the World Bank and the African Development Bank.”
The World Bank
Working with the World Bank, Fraym helped identify, quantify, and locate the most socioeconomically vulnerable communities in Pakistan – providing a baseline understanding of communities that would benefit most from targeted programming. In a recent blog entitled, Three ways geospatial data can help Pakistan during COVID-19, World Bank analysts used Fraym data for a mapping exercise to explore ‘risk of exposure and disease transmission’, ‘community behavior and access to information’, and ‘vulnerability to socioeconomic impacts.’
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The African Development Bank
This year Fraym also partnered with the African Development Bank to create a high-resolution map of the Bank’s development impact in seven West African countries. Looking at communities within 20 km of eight Bank-financed road projects, the map reveals how peoples’ lives were transformed. In Ghana, for example, 103,000 people living near the Awoshie-Pokuase road gained access to clean water. An extra 3,000 youth enrolled in school, and stunting fell by 10% among children under five. Innovations in predictive modeling are deepening the Bank’s understanding of how the $1.7 billion they invest every year in transport infrastructure is improving the lives of people across the African continent. For more examples of how the bank supports African development, see the 10th edition of the Annual Development Effectiveness Review.
Fraym
Fraym uses machine-learning models to generate hyper-local insights on communities around the globe. The company’s location-based data about population characteristics and behaviors is employed by governments and organizations tackling some of the world’s most pressing challenges. Using proprietary algorithms, Fraym produces datasets that cover a wide range of indicators including socio-economics, attitudes, media consumption, health, education, and access to services. This advanced geospatial data and analysis is available for 100+ countries at one square kilometer resolution—even in remote areas.
